Graal and Truffle: Modularity and Separation of Concerns as Cornerstones for Building a Multipurpose Runtime

Graal and Truffle:
Modularity and Separation of Concerns
as Cornerstones for Building a
Multipurpose Runtime
Thomas Wuerthinger
Oracle Labs
@thomaswue
24-April-2014,
Keynote at MODULARITY in Lugano

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.2
Disclaimer
The following is intended to provide some insight into a line of
research in Oracle Labs. It is intended for information purposes
only, and may not be incorporated into any contract. It is not a
commitment to deliver any material, code, or functionality, and
should not be relied upon in making purchasing decisions. The
development, release, and timing of any features or
functionality described in connection with any Oracle product or
service remains at the sole discretion of Oracle. Any views
expressed in this presentation are my own and do not
necessarily reflect the views of Oracle.

Agenda
§  Graal
§  Truffle
§  Community
§  Q&A

Dimensions of Extensibility
Architectures Host Runtimes
Compilation Policy Programming Languages
Graal
X86, SPARC, HSAIL, PTX, ... HotSpotVM, SubstrateVM, ...
Java, JavaScript, Python, R,
Ruby, C, Smalltalk, ...
Baseline, Mid-tier, Optimizing,
Vectorizing, ...

Modularity
Graal
Truffle
runtime-specificisa-specific
Graal extension
Graal/Truffle bridge
Truffle language
Fine-grained modular structure with Rich Client
Platforms like Eclipse or NetBeans as role models.

Specific to Host Runtime
§ Field/Array Access
–  object/array layout, read/write barriers, …
§ Allocation
–  garbage collector, thread-local buffer, …
§ Type Checks
–  class hierarchy organization, …
§ Locking
–  monitor system, monitor enter/exit, …
§ JDK intrinsifications
–  hashCode, clone, reflection, …
§ Invocations
§ Safepoints

Levels of Lowering
Java
Lowered
Runtime-Specific
ISA-Specific
JavaArrayStore
NullCheck Length BoundsCheck TypeCheck Store
Read WriteBarrierCmpUCmpNullCheck
Read&Check WriteShiftRead&CmpUCmp
Read
Write

Snippets for Graph Construction
int max(int a, int b) {
if (a > b) return a;
else return b;
}
Node max(ValueNode a, ValueNode b) {
IfNode ifNode = new IfNode(new IntegerLessThanNode(a, b));
ifNode.trueSuccessor().setNext(new ReturnNode(a));
ifNode.falseSuccessor().setNext(new ReturnNode(b));
return ifNode;
}
Manual construction:
Expression as snippet:
Data Code

Simple API
API ProviderAPI User
request
data
ü  Can capture statically x  Limited flexibility

Callback API
request including callback
callback with data
callback with data
callback with data
request returns
ü  High flexibility x  Cannot capture statically

Snippet API
request
code as data
ü  High flexibility ü  Can capture statically

Snippet Lifecycle
Bytecodes Prepared
IR Graph Specialized
IR Graphs
Preparation Specialization Instantiation
Once Few Times Many Times
...
aload_0
getfield
ifne 10
aload_1
arraylength
...
Frequency:
Java Bytecode Parsing
Node Intrinsification
Exhaustive Method Inlining
Constant Folding, Canonicalization
Graph Duplication
Node Intrinsification
Constant Folding, Canonicalization
Constant Parameter Replacement
Graph Duplication
Graph Inlining in Targe
Constant Folding, Cano
Steps:
Targ
with
...

Snippet Example: Convert
@Snippet
static int f2i(float input, int result) {
if (probability(SLOW_PATH,
result == Integer.MIN_VALUE)) {
if (Float.isNaN(input)) {
return 0;
} else if (input > 0.0f) {
return Integer.MAX_VALUE;
}
}
return result;
}

Agenda
§  Graal
§  Truffle
§  Community
§  Q&A

Technical Approach
Speculate and Optimize…
U
U U
U
U I
I I
G
G I
I I
G
G
Node Rewriting
for Profiling Feedback
AST Interpreter
Rewritten Nodes
AST Interpreter
Uninitialized Nodes
Compilation using
Partial Evaluation
Compiled Code
Node Transitions
S
U
I
D
G
Uninitialized Integer
Generic
DoubleString

Technical Approach
… and Deoptimize and Reoptimize!
I
I I
G
G I
I I
G
G
Deoptimization
to AST Interpreter
D
I D
G
G D
I D
G
G
Node Rewriting to Update
Profiling Feedback
Recompilation using
Partial Evaluation

Technical Approach
Three main parts for driving partial evaluation
§ Limit partial evaluation expansion
–  Annotation @SlowPath on a method stops the inclusion of a method
in the expansion.
§ Dynamic speculation
–  Call to CompilerDirectives.transferToInterpreter()
advises the partial evaluator to stop and place a deoptimization exit.
§ Global speculation
–  Assumption objects can be used for global speculations about the
system state. Checking the assumption in compiled code poses no
runtime overhead.

Peak Performance: JavaScript
Speedup relative to V8
1.0
1.5
0.6
0.7
0.9
2.6
0.5
1.4
0.8
1.0
0.7
0.8
1.0
0.7
1.1
1.6
0.6
1.1
1.2
0.9
0.0
0.5
1.0
1.5
2.0
2.5
3.0
richards
deltablue
crypto
raytrace
navier-stokes
splay
earley-boyer
box2d
gbem
u
C
om
posite
Truffle
SpiderMonkey
Selection of benchmarks from Google‘s Octane benchmark suite v1.0
latest versions of V8, Truffle, and SpiderMonkey as of December 2013

Peak Performance: C
Speedup relative to GCC O0
1
1
1
1
1
1
2.7
2.0
2.0
1.9
2.6
3.2
2.5
1.7
2.0
1.9
2.7
3.4
2.1
1.8
1.9
1.0
2.8
3.4
0
0.5
1
1.5
2
2.5
3
3.5
4
fannkuch-redux fasta mandelbrot meteor-contest nbody spectralnorm
GCC O0
GCC Best
Clang Best
TruffleC
Grimmer, Rigger, Schatz, Stadler, Mössenböck:
TruffleC: Dynamic Execution of C on the Java
Virtual Machine; to be submitted

Agenda
§  Graal
§  Truffle
§  Community
§  Q&A

Graal OpenJDK Project
Graal
Truffle
JavaScript
HotSpotVM
RRuby
Java Scala
Python Smalltalk C J
http://openjdk.java.net/projects/graal/
§ Development of Graal/Truffle core artifacts and APIs
§ Highly active: 30+ contributors over last 12 months
§ Highly modular: 80+ individual modules

Research Areas
Language
Implementation
General Language
Research
Compiler
Construction
Experimentation with new language
features, new languages, new
execution models
Language-independent instrumentation,
cross-language research, automatic partial
evaluation experiments
Core compiler construction research,
heterogenuous computing, advanced
architectures and backends
Truffle
Interpreters
Truffle
Graal

Graal/Truffle Related Research Projects (1)
§ TruffleRuby
–  Development in the JRuby repository (lead Chris Seaton).
–  https://github.com/jruby/jruby
–  http://blog.jruby.org/2014/01/truffle_graal_high_performance_backend/
§ FastR
–  Joint effort of a group from Purdue University (Prof. Jan Vitek) and a
team at Oracle Labs (lead Michael Haupt).
–  https://bitbucket.org/allr/fastr
§ ZipPy
–  Development by a group from University of California, Irvine (Prof.
Michael Franz).
–  https://bitbucket.org/ssllab/zippy
§ TruffleSOM
–  Development by Stefan Marr at: https://github.com/smarr/

Graal/Truffle Related Research Projects (2)
§ C and Language Interoperability
–  Experiment by students at JKU Linz (Matthias Grimmer and Manuel Rigger).
§ JavaScript
–  Effort done by the core Graal/Truffle team.
§ Debugging
–  Effort by Micheal van de Vanter from Oracle Labs.
§ SubstrateVM
–  Team at Oracle Labs led by Christian Wimmer is developing an alternative
host runtime.
§ Graal IR Instrumentation
–  Research by Yudi Zheng (USI Lugano) on instrumenting Graal IR.
§ GPU Offload
–  Research by Christopher Dubach et al. from the University of Edinburgh.
–  Graal is the compiler of choice for Project Sumatra (HSAIL/PTX offload).

Your Language or Compiler Extension?
graal-dev@openjdk.java.net
$ hg clone http://hg.openjdk.java.net/graal/graal
$ cd graal
$ ./mx --vm server build
$ ./mx ideinit
$ ./mx --vm server unittest SumTest
§  Truffle API License: GPLv2 with Classpath Exception
§  Graal License: GPLv2
https://wiki.openjdk.java.net/display/Graal/Main
§  Graal Resources

Acknowledgements
Oracle Labs
Danilo Ansaloni
Daniele Bonetta
Laurent Daynès
Erik Eckstein
Michael Haupt
Peter Kessler
David Leibs
Mick Jordan
Tom Rodriguez
Roland Schatz
Chris Seaton
Doug Simon
Lukas Stadler
Michael Van De Vanter
Adam Welc
Christian Wimmer
Christian Wirth
Mario Wolczko
Thomas Würthinger
Laura Hill
Interns
Miguel Garcia Gutierrez
Shams Imam
Stephen Kell
Gregor Richards
Rifat Shariyar
JKU Linz
Prof. Hanspeter Mössenböck
Stefan Anzinger
Gilles Duboscq
Josef Eisl
Matthias Grimmer
Christian Häubl
Josef Haider
Christian Humer
Christian Huber
David Leopoldseder
Manuel Rigger
Georg Schmid
Bernhard Urban
Andreas Wöß
University of Edinburgh
Christophe Dubach
Juan José Fumero Alfonso
Ranjeet Singh
Toomas Remmelg
LaBRI
Floréal Morandat
University of California, Irvine
Prof. Michael Franz
Codrut Stancu
Gulfem Savrun Yeniceri
Wei Zhang
Purdue University
Prof. Jan Vitek
Tomas Kalibera
Petr Maj 
Lei Zhao
T. U. Dortmund
Prof. Peter Marwedel
Helena Kotthaus
Ingo Korb
University of California, Davis
Prof. Duncan Temple Lang
Nicholas Ulle
And many more…

Q/A
graal-dev@openjdk.java.net
@thomaswue

Graal and Truffle: Modularity and Separation of Concerns as Cornerstones for Building a Multipurpose Runtime

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